System and method for image reconstruction

ABSTRACT

A system and method for image reconstruction are provided. A first region of an object may be determined. The first region may correspond to a first voxel. A second region of the object may be determined. The second region may correspond to a second voxel. Scan data of the object may be acquired. A first regional image may be reconstructed based on the scan data. The reconstruction of the first regional image may include a forward projection on the first voxel and the second voxel and a back projection on the first voxel.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application U.S. Ser.No. 16/448,052, filed on Jun. 21, 2019, which is a continuation in partof U.S. patent application U.S. Ser. No. 15/394,633, filed on Dec. 29,2016 (now U.S. Pat. No. 10,347,014), which claims priority of ChinesePatent Application No. 201610626367.3 filed on Aug. 2, 2016, ChinesePatent Application No. 201610626362.0 filed on Aug. 2, 2016, and PCTApplication PCT/CN2016/092881 filed on Aug. 2, 2016, the entire contentsof each of which are hereby incorporated by reference.

TECHNICAL FIELD

This present disclosure relates to a system and method for imagereconstruction, and more particularly, relates to a system and methodfor multi-resolution image reconstruction.

BACKGROUND

Positron emission tomography (PET) technology has been widely used inclinical examination and disease diagnosis in recent years. A wide-fieldPET device including a plurality of axial PET units has a wide field ofview and may be used to scan all or part of an object. Imagereconstruction is a key technology in the field of PET. However,traditional reconstruction techniques may be unable to simultaneouslyreconstruct images of different portions of a scanned object based ondifferent reconstruction parameters. Additional, a traditionalreconstruction technique may be complex and need a large amount ofcalculation resources. Thus, it may be desirable to develop an imagereconstruction method and system that may solve the above-mentionedproblems.

SUMMARY

An aspect of the present disclosure relates to a method for imagereconstruction. The method may include one or more of the followingoperations. A first region of an object may be determined. The firstregion may correspond to a first voxel. A second region of the objectmay be determined. The second region may correspond to a second voxel.Scan data of the object may be acquired. A first regional image may bereconstructed based on the scan data. The reconstruction of the firstregional image may include a forward projection on the first voxel andthe second voxel and a back projection on the first voxel.

In some embodiments, a second regional image may be reconstructed basedon the scan data. The reconstruction of the second regional image mayinclude a forward projection on the first voxel and the second voxel anda back projection on the second voxel.

In some embodiments, the reconstructing a first regional image mayinclude performing a first filtering on the first regional image. Thereconstructing a second regional image may include performing a secondfiltering on the second regional image.

In some embodiments, the reconstructing a first regional image mayinclude iteratively reconstructing the first regional image based on thescan data for a first number of iterations. The reconstructing a secondregional image may include iteratively reconstructing the secondregional image based on the scan data for a second number of iterations.

In some embodiments, the first number of iterations is different fromthe second number of iterations.

In some embodiments, the reconstructing a first regional image and thereconstructing a second regional image may be performed based on anOrdered Subset Expectation Maximization algorithm.

In some embodiments, the forward projection on the first voxel and thesecond voxel may be performed along a line of response.

In some embodiments, the method for image reconstruction may furtherinclude one or more of the following operations. Structure informationof the object may be acquired. The first region and the second regionmay be determined based on the structure information.

In some embodiments, the method for image reconstruction may furtherinclude one or more of the following operations. A first image matrixand a second image matrix may be determined. The first voxel may bestored in the first image matrix and the reconstructing a first regionalimage may include reconstructing the first image matrix. The secondvoxel may be stored in the second image matrix and the reconstructing asecond regional image may include reconstructing the second imagematrix.

In some embodiments, the method for image reconstruction may furtherinclude generating a lookup table. The lookup table may record acorrelation between the first image matrix and the first voxel, or acorrelation between the second image matrix and the second voxel.

In some embodiments, the correlation between the first image matrix andthe first voxel may include a correlation between the first image matrixand a rearranged first voxel.

In some embodiments, a first voxel size corresponding to the firstregion may be set, and a second voxel size corresponding to the secondregion may be set. A merged matrix may be determined. The voxel size ofthe merged matrix may equal to the smaller voxel size of the first voxelsize and the second voxel size. The first image matrix and the secondimage matrix may be filled into the merged matrix.

An aspect of the present disclosure relates to another method for imagereconstruction. The method may include one or more of the followingoperations. An image matrix which corresponds to a scanned region may bedetermined. The scanned region may include at least one sub-scannedregion. The image matrix may be divided into a plurality of sub-imagematrixes. A sub-image matrix of the one or more sub-image matrixes maycorrespond to a sub-scanned region of the at least one sub-scannedregion. One or more of the sub-image matrixes may be transformed togenerate one or more transformed matrixes. The one or more of thesub-image matrixes may be reconstructed based on the one or moretransformed matrixes. The image matrix may be reconstructed based on theone or more reconstructed sub-image matrixes.

In some embodiments, the transforming one or more of the sub-imagematrixes may include compressing the one or more of the sub-imagematrixes or rearranging the one or more of the sub-image matrixes.

In some embodiments, the method for image reconstruction may furtherinclude generating a lookup table of the image matrix and the one ormore of the sub-image matrixes. The lookup table may record a manner ofthe compressing the one or more of the sub-image matrixes or a manner ofthe rearranging the one or more of the sub-image matrixes.

In some embodiments, the transforming one or more of the sub-imagematrixes may include decompressing the one or more sub-image matrixesbased on the lookup table.

An aspect of the present disclosure relates to a system for imagereconstruction. The system may include an imaging device and aprocessor. The imaging device may be configured to acquire scan data ofan object. The processor may include an acquisition module and areconstruction module. The acquisition module may be configured toacquire information regarding a first region of the object, a size of afirst voxel corresponding to the first region, a second region of theobject and a size of a second voxel corresponding to the second region.The reconstruction module may be configured to reconstruct the firstregional image. The reconstruction of the first regional image mayinclude a forward projection on the first voxel and the second voxel;and a back projection on the first voxel.

In some embodiments, the reconstruction module may be configured toreconstructing a second regional image based on the scan data. Thereconstruction of the second regional image may include a forwardprojection on the first voxel and the second voxel; and a backprojection on the second voxel.

In some embodiments, the reconstruction module may include an imagematrix generation unit. The image matrix generation unit may beconfigured to determine a first image matrix and a second image matrix.The first voxel may be stored in the first image matrix. Thereconstructing a first regional image may include reconstructing thefirst image matrix. The second voxel may be stored in the second imagematrix. The reconstructing a second regional image may includereconstructing the second image matrix.

In some embodiments, the reconstruction module may include an imagematrix processing unit. The image matrix processing unit may beconfigured to perform one or more operations on the first image matrixand the second image matrix. The operations may include image matrixrotation, image matrix compression, image matrix decompression, imagematrix rearrangement, image matrix filling and image matrix merging.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1A is a block diagram of a system for image reconstructionaccording to some embodiments of the present disclosure;

FIG. 1B illustrates an architecture of a computer on which a specializedsystem incorporating the present teaching may be implemented;

FIG. 2 is a block diagram of an imaging processing device according tosome embodiments of the present disclosure;

FIG. 3 is a block diagram of a reconstruction module according to someembodiments of the present disclosure;

FIG. 4 is a flowchart illustrating an exemplary process for imagereconstruction according to some embodiments of the present disclosure.

FIG. 5 is a block diagram of a post-processing module according to someembodiments of the present disclosure;

FIG. 6A is a flowchart illustrating an exemplary process forpost-processing according to some embodiments of the present disclosure;

FIG. 6B is a flowchart illustrating an exemplary process forpost-processing according to some embodiments of the present disclosure;

FIG. 7A and FIG. 7B are schematic diagrams illustrating a correlationbetween voxels and matrixes according to some embodiments of the presentdisclosure;

FIG. 8 is a schematic diagram illustrating a match between imagingmodules according to some embodiments of the present disclosure;

FIG. 9 is a block diagram of an image matrix processing unit accordingto some embodiments of the present disclosure;

FIG. 10A, FIG. 10B, and FIG. 10C are schematic diagrams illustrating anexemplary process for processing an image matrix according to someembodiments of the present disclosure;

FIG. 11 is a flowchart illustrating an exemplary process forreconstructing an image matrix according to some embodiments of thepresent disclosure; and

FIG. 12 is a flowchart illustrating an exemplary process for processingan image matrix according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of example in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

As used herein, the singular forms “a,” “an,” and “the” may be intendedto include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprise,” and/or “include,” when used in this disclosure, specify thepresence of integers, devices, behaviors, stated features, steps,elements, operations, and/or components, but do not exclude the presenceor addition of one or more other integers, devices, behaviors, features,steps, elements, operations, components, and/or groups thereof.

Although the present disclosure makes various references to certainmodules in the system according to some embodiments of the presentdisclosure, any number of different modules may be used and run on theclient and/or server. The modules are illustrative only, and differentaspects of the systems and methods may use different modules.

A flowchart is used in the present disclosure to describe operationsperformed by a system according to some embodiments of the presentdisclosure. It should be understood that the preceding or followingoperations are not necessarily performed exactly in sequence. Instead,various steps may be processed in reverse order or simultaneously. Atthe same time, other steps may be added to the operations, or removingone or more steps from the operations.

The term “scanned region” may refer to a physical region to be scanned.The term “reconstruction region” may refer to a physical regioncorresponding to a reconstructed image matrix. Unless the contextclearly indicates an exception, the terms “scanned region,”“reconstruction region,” and “physical region” may have the same meaningand may be used interchangeably in the present disclosure.

The term “element” may refer to the smallest component in an imagematrix. The term “voxel” may refer to the smallest component in aphysical region. The “element” in an image matrix may correspond to the“voxel” in a physical region in the present disclosure.

FIG. 1A is a block diagram of an image reconstruction system accordingto some embodiments of the present disclosure. System 100 may include animaging processing device 120, a network 130 and an imaging device 110.In some embodiments, at least part of image processing device 120 may beimplemented on computer 100 b shown in FIG. 1B.

Imaging device 110 may be a single modality imaging system, e.g., aDigital Subtraction Angiography (DSA) system, a Magnetic ResonanceAngiography (MRA) system, a Computed Tomography Angiography (CTA), aPositron Emission Tomography (PET) system, a Single Photon EmissionComputed Tomography (SPECT) system, a Computed Tomography (CT) system, aDigital Radiography (DR) system, etc. The system may be a multi-modalityimaging system, e.g., a Computed Tomography-Positron Emission Tomography(CT-PET) system, a Positron Emission Tomography-Magnetic ResonanceImaging (PET-MRI) system, a Single Photon Emission ComputedTomography-Positron Emission Tomography (SPECT-PET) system, a DigitalSubtraction Angiography-Magnetic Resonance Imaging (DSA-MR) system, etc.

Imaging processing device 120 may acquire information and process theacquired information to reconstruct an image. The acquired informationmay be obtained from imaging device 110, network 130, or be produced byimaging processing device 120. Imaging processing device 120 may be anelectronic device or a server. The electronic device may include aportable computer, a tablet, a mobile phone, an intelligent terminaldevice, or the like, or a combination thereof. Imaging processing device120 may be centralized, such as a data center. Imaging processing device120 may be distributed, such as a distributed system. Imaging processingdevice 120 may be local or remote. In some embodiments, the acquiredinformation may include image information of one or more objects. Theimage information may be acquired by scanning or otherwise. In someembodiments, imaging processing device 120 may be implemented oncomputer 100 b shown in FIG. 1B.

In some embodiments, imaging processing device 120 may include a centralprocessing unit (CPU), an application specific integrated circuit(ASIC), an application specific instruction set processor (ASIP), aphysics processing unit (PPU), a digital processing processor (DSP), afield-programmable gate array (FPGA), an programmable logic device(PLD), a processor, a microprocessor, a controller, a microcontroller,or the like, or a combination thereof.

Network 130 may be a single network or a combination of a plurality ofdifferent kinds of networks. For example, the network may be a localarea network (LAN), a wide area network (WAN), a public network, apersonal network, a private network, a public switched telephone network(PSTN), the internet, a wireless network, a virtual network, or thelike, or a combination thereof. Network 130 may include multiple networkaccess points (NAP). The wired network may including using a metalcable, an optical cable, a hybrid cable, an interface, or the like, or acombination thereof. The wireless network may include a local areanetwork (LAN), a wide area network (WAN), a Bluetooth, a ZigBee, a nearfield communication (NFC), or the like, or a combination thereof.Network 130 that may be described herein is not exhaustive and are notlimiting. It should be noted that the above description about network130 is merely provided for the purposes of illustration, and notintended to limit the scope of the present disclosure.

Imaging device 110 may include one or more devices that may scan one ormore objects. For instance, the one or more devices may be used in, butnot limited to, medical applications (e.g., a medical testingtechnology, etc.). In some embodiments, exemplary medical testingtechnologies may include magnetic resonance imaging (MRI), X-raycomputed tomography (CT), positron emission computed tomography (PET),and single photon emission computed tomography (SPECT), or the like, orany combination thereof. In some embodiments, the object to be scannedmay be an organ, an organism, a compound, a dysfunction, a tumor, or thelike, or any combination thereof. In some embodiments, the object to bescanned may be the head, chest, bones, blood vessels of a human body, orthe like, or any combination thereof. In some embodiments, imagingdevice 110 may include one or more imaging modules. The one or moreimaging modules may include one or more detectors. The one or moredetectors may be continuously placed around the object to be scanned.

In some embodiments, imaging device 110 and imaging processing device120 may be connected to or communicated with each other. In someembodiments, imaging device 110 may transmit information to imagingprocessing device 120 via network 130. In some embodiments, imagingdevice 110 may directly transmit information to imaging processingdevice 120. In some embodiments, imaging processing device 120 mayprocess information stored in itself.

FIG. 1B illustrates an architecture of a computer 100 b on which aspecialized system incorporating the present teaching may beimplemented. Such a specialized system incorporating the presentteaching has a functional block diagram illustration of a hardwareplatform that may include user interface elements. Computer 100 b may bea general purpose computer or a special purpose computer. Computer 100 bmay be used to implement any component of image processing as describedherein. For example, image processing device 120 may be implemented on acomputer such as computer 100 b, via its hardware, software program,firmware, or a combination thereof. Although only one such computer isshown, for convenience, the computer functions relating to imageprocessing as described herein may be implemented in a distributedfashion on a number of similar platforms, to distribute the processingload. In some embodiments, computer 100 b may be used as imagingprocessing device 120 shown in FIG. 1 .

Computer 100 b, for example, may include communication (COM) ports 111connected to and from a network connected thereto to facilitate datacommunications. Computer 100 b may also include a central processingunit (CPU) 105, in the form of one or more processors, for executingprogram instructions. The exemplary computer platform may include aninternal communication bus 104, program storage, and data storage ofdifferent forms, e.g., disk 108, read-only memory (ROM) 106, or randomaccess memory (RAM) 107, for various data files to be processed and/orcommunicated by the computer, as well as possibly program instructionsto be executed by CPU 105. Computer 100 b may also include an I/Ocomponent 109, supporting input/output flows between the computer andother components therein such as user interface elements 113. Computer100 b may also receive programming and data via network communications.

Aspects of the methods of the image processing and/or other processes,as described herein, may be embodied in programming. Program aspects ofthe technology may be thought of as “products” or “articles ofmanufacture” typically in the form of executable code and/or associateddata that is carried on or embodied in a type of machine readablemedium. Tangible non-transitory “storage” type media include any or allof the memory or other storage for the computers, processors, or thelike, or associated modules thereof, such as various semiconductormemories, tape drives, disk drives and the like, which may providestorage at any time for the software programming.

All or portions of the software may at times be communicated through anetwork such as the Internet or various other telecommunicationnetworks. Such communications, for example, may enable loading of thesoftware from one computer or processor into another, for example, froma management server or host computer of an image reconstruction systeminto the hardware platform(s) of a computing environment or other systemimplementing a computing environment or similar functionalities inconnection with image processing. Thus, another type of media that maybear the software elements includes optical, electrical andelectromagnetic waves, such as used across physical interfaces betweenlocal devices, through wired and optical landline networks and overvarious air-links. The physical elements that carry such waves, such aswired or wireless links, optical links or the like, also may beconsidered as media bearing the software. As used herein, unlessrestricted to tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

A machine-readable medium may take many forms, including but not limitedto, a tangible storage medium, a carrier wave medium or physicaltransmission medium. Non-volatile storage media include, for example,optical or magnetic disks, such as any of the storage devices in anycomputer(s), or the like, which may be used to implement the system orany of its components shown in the drawings. Volatile storage media mayinclude dynamic memory, such as a main memory of such a computerplatform. Tangible transmission media may include coaxial cables; copperwire and fiber optics, including the wires that form a bus within acomputer system. Carrier-wave transmission media may take the form ofelectric or electromagnetic signals, or acoustic or light waves such asthose generated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer-readable media may include, forexample: a floppy disk, a flexible disk, hard disk, magnetic tape, anyother magnetic medium, a CD-ROM, DVD or DVD-ROM, any other opticalmedium, punch cards paper tape, any other physical storage medium withpatterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM, any othermemory chip or cartridge, a carrier wave transporting data orinstructions, cables or links transporting such a carrier wave, or anyother medium from which a computer may read programming code and/ordata. Many of these forms of computer readable media may be involved incarrying one or more sequences of one or more instructions to a physicalprocessor for execution.

Those skilled in the art will recognize that the present teachings areamenable to a variety of modifications and/or enhancements. For example,although the implementation of various components described herein maybe embodied in a hardware device, it may also be implemented as asoftware only solution—e.g., an installation on an existing server. Inaddition, image processing as disclosed herein may be implemented as afirmware, firmware/software combination, firmware/hardware combination,or a hardware/firmware/software combination.

FIG. 2 is a block diagram of an imaging processing device according tosome embodiments of the present disclosure. Imaging processing device120 may include an acquisition module 210, a reconstruction module 220,a post-processing module 230, a display module 240, and a storage module250.

Acquisition module 210 may acquire information. The information may beacquired by scanning an object (e.g., scanning an object by imagingdevice 110), or be acquired from other source (e.g., storage module 250,network 130, etc.). The acquired information may include voxel data,voxel counts (or referred to as number of voxels), matrix, image,vector, vector database, or the like, or a combination thereof.

Reconstruction module 220 may reconstruct the information acquired byacquisition module 210. The information reconstruction may includegenerating an image matrix based on the acquired information. The imagematrix may correspond to all or one or more parts of a scanned object.

In some embodiments, the information reconstruction may includedetermining one or more scanned regions and its corresponding one ormore voxels. The one or more voxels may correspond to one or moreelements in one or more image matrixes. The one or more image matrixesmay be reconstructed iteratively based on the acquired information. Insome embodiments, the iterative reconstruction of an image matrix mayinclude performing one or more forward projections and/or one or moreback projections. In some embodiments, the information reconstructionmay include removing part of the information to improve calculationefficiency and/or storage efficiency. Merely by way of example, theinformation may be transformed into an image matrix. The image matrixmay be compressed and/or rearranged to improve calculation efficiencyand/or storage efficiency.

Post-processing module 230 may perform one or more post-processingoperations on the reconstructed information generated by reconstructionmodule 220. In some embodiments, a reconstruction matrix based on one ormore voxels may be post-processed to generate an image or an imagematrix of all or one or more parts of a scanned object. The one or morepost-processing operations of the image matrix may include filtering,noise reduction, merging, dividing, or the like, or a combinationthereof.

Display module 240 may display an image generated by post-processingmodule 230. In some embodiments, display module 240 may include adisplay device (e.g., a display screen, etc.). In some embodiments,display module 240 may perform one or more processing operations on theimage before displaying. The one or more processing operation mayinclude image rendering, image scaling, image rotating, maximumintensity projection, or the like, or a combination thereof. In someembodiments, display module 240 may include an input device. The inputdevice may include a keyboard, a touch screen, a touchpad, a mouse, aremote control, or the like, or a combination thereof. In someembodiments, a user may input a parameter, and/or an initial conditionfor image displaying and/or image processing by the input device.

In some embodiments, a user may set a displaying parameter or processthe image on display module 240. Merely by way of example, the user maychoose the displayed content. The displayed content may include atwo-dimensional image, a three-dimensional image, an image correspondingto scanned data, a control interface, an input interface, images ofdifferent regions, a process of image reconstruction, and a result ofimage reconstruction, or the like, or any combination thereof. Asanother example, the user may enlarge an image or a portion thereof,extract a portion of an image, and/or shrink an image.

Storage module 250 may store data. The stored data may be obtained fromimaging device 110, network 130, and/or other modules or units ofimaging processing device 120 (e.g., acquisition module 210,reconstruction module 220, post-processing module 230, display module240, etc.). Storage module 250 may be any device that may storeinformation. For example, storage module 250 may be a device thatutilizes electrical energy to store information, such as a random accessmemory (RAM) or a read only memory (ROM). Storage module 250 may be adevice that utilizes magnetic energy to store information, such as ahard disk, a floppy disk, a magnetic tape, a magnetic core memory, abubble memory, a USB disk, a flash memory. Storage module 250 may be adevice that utilizes optical energy to store information, such as CD orDVD, or the like. The access mode of storage module 250 may be randomaccess, serial access storage, or read only storage, or the like, or anycombination thereof. Storage module 250 may be a non-persistent memoryor a permanent memory.

Storage module 250 may be connected with acquisition module 210,reconstruction module 220, post-processing module 230, display module240, or other related modules. In some embodiments, storage module 250may be connected with one or more virtual storage resources via network130. The virtual storage resource may include a cloud storage, a virtualprivate network, and/or other virtual storage resources. The stored datamay include various forms of data. The data may include a value, asignal, an image, a command, an algorithm, a program, or the like, or acombination thereof.

For persons having ordinary skills in the art, various modifications andchanges in the forms and details of the application of the above methodand system may occur without departing from the principles in thepresent disclosure. However, those variations and modifications alsofall within the scope of the present disclosure. For example, the abovemodules may refer to different modules in a system, or one module mayrealize the function of two or more modules of the above modules. Insome embodiments, storage module 250 may be contained in any one or moremodules. In some embodiments, acquisition module 210 and display module240 may form an input/output module. In some embodiments, reconstructionmodule 220 and post-processing module 230 may form an image generationmodule.

FIG. 3 is a block diagram of a reconstruction module according to someembodiments of the present disclosure. Reconstruction module 220 mayinclude a parameter setting unit 310, a region selection unit 320, animage matrix generation unit 340, an image matrix processing unit 350, acalculation unit 360, a distribution unit 370, or the like, or anycombination thereof.

Parameter setting unit 310 may set a parameter in the process ofreconstruction. The parameter may include the size of a reconstructionregion, the position of the reconstruction region, the voxel size (orreferred to as a size of a voxel) of the reconstruction region, theiterative algorithm to be used, and number of iterations, or thetermination condition to stop iterations, or the like, or anycombination thereof. In some embodiments, the parameter may be acquiredfrom storage module 250. In some embodiments, a user may set theparameter via acquisition module 210 or display module 240. In someembodiments, a default value of the parameter may be stored in parametersetting unit 310. The default value may be used when the parameter isnot set.

Region selection unit 320 may select a reconstruction region. Thereconstruction region may be selected by selecting size and/or positionof the reconstruction region. In some embodiments, region selection unit320 may acquire one or more parameters about the size and/or position ofthe reconstruction region from parameter setting unit 310. In someembodiments, region selection unit 320 may store default parameter(s)about one or more scanned parts (e.g., a head cavity, a chest cavity, anabdominal cavity, etc.). The default parameter(s) may be acquired oradjusted. In some embodiments, region selection unit 320 may be combinedwith display module 240. For instance, a user may select a region to bescanned and/or to be reconstructed in the image displayed by displaymodule 240. Region selection unit 320 may scan or reconstruct theselected region.

Image matrix generation unit 340 may generate one or more imagematrixes. The one or more image matrixes may correspond to one or morescanned regions. In some embodiments, there is a one-to-onecorrespondence between the image matrix and the scanned region. In someembodiments, one element in the image matrix may correspond to one voxelsize in the scanned region. The voxel size may include an X-rayattenuation coefficient, a gamma-ray attenuation coefficient, a hydrogenatom density, or the like, or any combination thereof. In someembodiments, the voxel size may be modified and/or updated in theprocess of iterative reconstruction. In some embodiments, the voxel sizemay be converted into the gray scale or RGB chroma of an image. Forexample, the image matrix may correspond to an image and/or be convertedinto an image.

Image matrix processing unit 350 may perform a processing operation onan image matrix. The processing operation may include dividing an imagematrix into a plurality of sub-image matrixes, image matrix rotation,image matrix, image matrix compression, image matrix decompression,image matrix rearrangement, image matrix inverse rearrangement, imagematrix filling, image matrix merging, or the like, or any combinationthereof. The image matrix rotation may include rotating an image matrixclockwise or counterclockwise.

The image matrix compression may include removing one or more elementsfrom the image matrix. In some embodiments, the voxel corresponding tothe removed element may be not penetrated by one or more rays (e.g., oneor more lines of response in a PET system, x-rays in a CT system, etc.).The value of the removed element may be set to zero or another fixedvalue in image reconstruction. In some embodiments, the removed elementmay be an element that satisfies one or more conditions. For example,the removed element may be an element whose value is below a threshold,or that is located at a certain position in the matrix.

The image matrix decompression may include adding one or more elementsinto the image matrix. In some embodiments, one or more elements thatare removed in image matrix compression may be added back to theiroriginal positions. In some embodiments, the value of the element mayremain the same in image matrix compression or image matrixdecompression.

The image matrix rearrangement may include moving an element of an imagematrix from a first position to a second position in the image matrix.In some embodiments, the element(s) with a certain characteristic may bearranged to a certain position or area including a cluster of positions.Correspondingly, the inverse image matrix rearrangement may includemoving all or a portion of the moved elements back to the originalposition(s). In some embodiments, the value of the element may remainthe same when it is arranged or rearranged.

The image matrix filling may include filling a null element in an imagematrix with a value according to a certain rule or algorithm. Merely byway of example, in a PET system, an element in an image matrix thatcorresponds to a voxel penetrated by a line of response may be filledbased on the position of the voxel. In some embodiments, the imagematrix filling may be performed according to the counts of detectorsalong a line of response and the effect of the voxels being penetratedby the line of response on the counts of detectors. As another example,in a computed tomography system, an element in an image matrix thatcorresponds to a voxel penetrated by an X-ray may be filled based on theposition of the voxel.

The image matrix division may include dividing an image matrix into aplurality of sub-image matrixes. In some embodiments, a sub-image matrixmay include a portion of the elements in the divided image matrix. Insome embodiments, a sub-image matrix may include a scanned region thatis penetrated by one or more lines of response. Similarly, a line ofresponse may penetrate one or more scanned regions corresponding to oneor more sub-image matrixes.

The image matrix merging may include merging a plurality of sub-imagematrixes into an image matrix. In some embodiments, an image matrix maybe divided into a plurality of sub-image matrixes for processing, andthen the plurality of sub-image matrixes may be merged back into theimage matrix.

Calculation unit 360 may calculate element values in an image matrix aswell as other values. In some embodiments, calculation unit 360 maycalculate element values in an image matrix corresponding to a scannedregion penetrated by one or more lines of response, and the calculationmay be performed based on the counts of detectors along the one or morelines of response. In some embodiments, calculation unit 360 may includea main computing node and one or more subordinate computing nodes. Insome embodiments, each of the one or more subordinate computing nodesmay generate a sub-image matrix corresponding to a sub-scanned region.The sub-scanned region may be scanned by one or more detectors. In someembodiments, the subordinate computing node may compute the values ofthe elements in the sub-image matrix corresponding to a sub-scannedregion based on the counts of detectors corresponding to the sub-scannedregion. In some embodiment, the main computing node may merge the valuesof the elements in different sub-image matrixes corresponding to a voxelin a sub-scanned region. For example, a voxel may correspond to aplurality of sub-image matrixes. The values of the elements in theplurality of sub-image matrixes corresponding to the voxel may berespectively computed by the subordinate computing nodes. The maincomputing node may merge the values of the elements in the plurality ofsub-image matrixes to determine the size of the voxel.

Allocation unit 370 may distribute computational tasks to differentcomputing nodes of calculation unit 360. The computing nodes may includeone or more main computing nodes and one or more subordinate computingnodes. In some embodiments, allocation unit 370 may match or groupdetectors and determine the size and position of a sub-scanned regioncorresponding to detector pairs or detector groups. In some embodiments,allocation unit 370 may distribute the computational tasks of thesub-image matrixes to different subordinate computing nodes.

For persons having ordinary skills in the art, multiple variations andmodifications to reconstruction module 220 may be made without departingfrom the principles of the system and method for image reconstruction inthe present disclosure. However, those variations and modifications alsofall within the scope of the present disclosure. For example, in someembodiments, image matrix generation unit 340 and image matrixprocessing unit 350 may form an image matrix unit. In some embodiments,reconstruction module 220 may not include calculation unit 360 and thefunction of calculation unit 360 may be realized by other units.

FIG. 4 is a flowchart illustrating an exemplary process for imagereconstruction according to some embodiments of the present disclosure.In some embodiments, process 400 may be performed by imaging processingdevice 120. In some embodiments, at least part of process 400 may beperformed by computer 100 b shown in FIG. 1B. As shown in FIG. 4 , in402, imaging processing device 120 may acquire structural information ofan object. For example, the structural information may includeinformation about the outline or appearance of the object. In someembodiments, 402 may be performed by acquisition module 210. Forexample, the structural information may be acquired by scanning theobject. The scanning may be performed by a CT system, an MR system, or aPET system, etc.

In 404, a first region and a size of first voxel corresponding to thefirst region may be determined according to the structural informationof the object. Operation 404 may be performed by acquisition module 210.In some embodiments, the first region may correspond to the entireobject. In some embodiments, the size of the first voxel may be storedin a first image matrix M₀ as a first element.

In 406, a second region and a size of second voxel corresponding to thesecond region may be determined according to the structural informationof the object. Operation 406 may be performed by acquisition module 210.In some embodiments, the second region may correspond to a portion ofthe object. In some embodiments, the size of the second voxel may bestored in a second image matrix M₁ as a second element. In someembodiments, the size of the second voxel may be smaller than the sizeof the first voxel. In some embodiments, the second region maycorrespond to the region that needs to be imaged with high resolution.

In 408, imaging processing device 120 may acquire scan information ofthe object. For example, imaging processing device 120 may acquire scaninformation by imaging device 110. In some embodiments, imaging device110 may include a PET device. As another example, the scan informationmay be acquired from storage module 250. As a further example, the scaninformation may be acquired from a remote storage module (e.g., a clouddisk) via network 130.

After acquiring scan information of the object, imaging processingdevice 120 may reconstruct the first image matrix M₀ to generate a firstregional image in 410, and reconstruct the second image matrix M₁ togenerate a second regional image in 412. In some embodiments, the firstimage matrix M₀ and the second image matrix M₁ may be reconstructedbased on an iterative reconstruction algorithm.

Merely by way of examples, the first image matrix M₀ and the secondimage matrix M₁ may be reconstructed based on an Ordered SubsetExpectation Maximization (OSEM) algorithm. The OSEM algorithm may beperformed according to Equation (1) below:f _(jm) ^((n+1)) =f _(jm) ^((n)) ·B(y _(i) ,F),  (1)wherein i is a serial number of a line of response (i.e., detectorpair), m is a serial number of an image matrix to be reconstructed, j isa serial number of an element in the image matrix m, f_(jm) ^((n)) is avalue of the element j in the image matrix m at the n^(th) iteration,y_(i) is the number of the lines of response i, F is a forwardprojection coefficient, B(y_(i), F) is a back projection coefficient.

In some embodiments, the OSEM algorithm may include one or moreoperations. The one or more operations may include performing a forwardprojection on an image matrix (e.g., performing a forward projection onthe voxel corresponding to an element in an image matrix), calculating acorrection coefficient, performing a back projection on an image matrix(e.g., performing a back projection on the voxel corresponding to anelement in an image matrix), updating an image matrix, etc. Moredescriptions regarding the one or more operations may be found elsewherein the present disclosure. See, e.g., FIG. 11 and FIG. 12 and thedescription thereof.

In some embodiments, the first image matrix M₀ may be reconstructed togenerate a first regional image. The reconstruction of the first imagematrix M₀ may include a forward projection of the first voxel and thesecond voxel, and a back projection of the first voxel. The second imagematrix M₁ may be reconstructed to generate an image of a second regionalimage. The reconstruction of the second image matrix M₁ may include aforward projection of the first voxel and the second voxel, and a backprojection of the second voxel. In some embodiments, the sizes of thefirst voxel and the second voxel may be different or the same.

A forward projection may be performed on an image matrix to generate adetection result of a detector. In some embodiments, a forwardprojection coefficient may be determined according to Equation (2)below:F=Σ _(m)Σ_(k) c _(ikm) f _(km) ^((n)),  (2)wherein k is a serial number of element related to the line of responsei in the image matrix m, and c_(ikm) represents the sensitivity of theline of response i to the element j in the image matrix m. In someembodiments, different image matrixes may correspond to different voxelsizes. For example, a line of response may penetrate a first regioncorresponding to a first voxel and a second region corresponding to asecond voxel. The forward projection on the image matrix may include aforward projection of the first voxel and a forward projection of thesecond voxel according to the Equation (2).

A correction coefficient may be calculated. The correction coefficientmay be a ratio of the number of lines of response to a forwardprojection value of the reconstructed image along the line of response,i.e.

$\frac{y_{i}}{F}.$

A back projection may be performed based on the correction coefficientto update the image matrix. The back projection may be performedaccording to Equation (3) below: as the formula below:

$\begin{matrix}{{{B_{m}\left( {y_{i},F} \right)} = {\frac{1}{\sum_{i}{\sum_{k}c_{ikm}}}{\sum_{i}{\sum_{k}{c_{ikm}\frac{y_{i}}{F}}}}}},} & (3)\end{matrix}$

In some embodiments, the numbers of iterations of different imagescorresponding to different image matrixes may be different. For example,to reconstruct an image matrix of the body of a patient, the iterationmay be performed twice. As another example, to reconstruct an imagematrix of the brain of the patient, the iteration may be performed forfour times.

A predetermined number of iterations performed to reconstruct an imagematrix may be denoted as d(m), wherein m is a serial number of the imagematrix number. m may be integer equal to or greater than 0. The Equation(3) and Equation (1) may be rewritten as Equation (4) and Equation (5)below, respectively:

$\begin{matrix}{{{B\left( {y_{i},F} \right)} = \begin{Bmatrix}{B_{m}\left( {y_{i},F} \right)} & {{d(m)} > n} \\1 & {{d(m)} \leq n}\end{Bmatrix}},{and}} & (4)\end{matrix}$ $\begin{matrix}{{f_{jm}^{({n + 1})} = {f_{jm}^{(n)} \cdot {B\left( {y_{i},F} \right)}}},{{d(m)} \geq n},} & (5)\end{matrix}$wherein n is the current iteration number. If the predetermined numberof iterations is greater than the current iteration number n, the imagematrix may be processed and thereby updated during the next iteration.If the predetermined number of iterations is less than or equal to thecurrent iteration number n, the iteration may be stopped and the imagecorresponding to the current image matrix may be output.

After acquiring the first image matrix M₀ and the second image matrixM₁, imaging processing device 120 may convert the first image matrix M₀into a first regional image and the second image matrix M₁ into a secondregional image based on the value of an element in the image matrix. Thevalue of an element in the image matrix may be (e.g., converted into)the gray scale or RGB chroma of the voxel in the image. Imagingprocessing device 120 may then perform a post-processing operation onthe first regional image and the second regional image. Moredescriptions regarding the post-processing operation may be foundelsewhere in the present disclosure. See, e.g., FIG. 6A and FIG. 6B, andthe description thereof.

FIG. 5 is a block diagram of a post-processing module according to someembodiments of the present disclosure. Post-processing module 230 mayinclude a filtering unit 510, a classifying unit 520, and a merging unit530.

Filtering unit 510 may be configured to filter an image matrix, or thedata, or image corresponding to the image matrix, etc. The filteringoperation may be performed based on one or more filtering algorithm. Thefiltering algorithm may include a Gaussian filtering algorithm, a Metzfiltering algorithm, a Butterworth filtering algorithm, a Hammingfiltering algorithm, a Hanning filtering algorithm, a Parzen filteringalgorithm, a Ramp filtering algorithm, a Shepp-logan filteringalgorithm, a Wiener filtering algorithm, or the like, or a combinationthereof. In some embodiments, different scanned region or differentparts of a scanned object may be filtered based on different filteringalgorithms. For example, the Metz filtering algorithm may be applied inbrain scanning, and the Gaussian filtering algorithm may be applied inbody scanning.

Classifying unit 520 may store one or more filtered image matrixes indifferent matrixes based on the voxel sizes of the one or more filteredimage matrixes. In some embodiments, the filtered image matrixes thathave the same or similar voxel sizes may be stored in the same matrixes.

Merging unit 530 may merge a plurality of image matrixes correspondingto different physical regions with different voxel sizes. In someembodiments, a merged matrix may be generated in the merging of imagematrixes. For example, the merged matrix may correspond to the largestregion of the regions corresponding to the image matrixes to be merged.As another example, the voxel size of the merged matrix may equal to theleast voxel size of the voxel sizes corresponding to the image matrixesto be merged. In some embodiments, the smaller voxel size, the higherthe resolution is. In some embodiments, the image matrixes to be mergedmay be interpolated to generate a high-resolution image. Theinterpolation of the image matrixes may include estimating a size of avoxel that is null in the high-resolution image when a low-resolutionimage is converted to a high-resolution image based on an interpolationalgorithm. Exemplary interpolation algorithm may include a bilinearinterpolation algorithm, a bi-cubic interpolation algorithm, a fractalinterpolation algorithm, a natural neighbor interpolation algorithm, anearest neighbor interpolation algorithm, a minimum curvature algorithm,a local polynomial regression algorithm, or the like, or a combinationthereof.

FIG. 6A and FIG. 6B are two flowcharts illustrating exemplary processesfor post-processing according to some embodiments of the presentdisclosure. Process 600 a and process 600 b may be performed bypost-processing module 230. As shown in FIG. 6A, in 602, a reconstructedimage matrix may be filtered. The filtering of the reconstructed imagematrix may be performed based on the Gaussian filtering algorithm, theMetz filtering algorithm, the Butterworth filtering algorithm, theHamming filtering algorithm, the Hanning filtering algorithm, the Parzenfiltering algorithm, the Ramp filtering algorithm, the Shepp-loganfiltering algorithm, the Wiener filtering algorithm, or the like, or acombination thereof. In some embodiments, different scanned region ordifferent parts of a scanned object may be filtered based on differentfiltering algorithms. For example, the Metz filtering algorithm may beapplied to a brain scan, and the Gaussian filtering algorithm may beapplied to a body scan.

In 604, the filtered reconstructed image matrixes may be classified intoone or more layers. For example, the image matrixes may be classifiedaccording to voxel sizes of the image matrixes. In some embodiments, theclassified image matrixes may be stored in a DICOM file. The DICOM filemay record the image matrixes, the classification information of theimage matrixes, and the values of voxels of the image matrixes. In someembodiments, the classification information of an image matrix mayinclude its voxel size. Merely by way of example, the image matrixes maybe classified into two layers, a first layer (or a higher layer) and asecond layer (or a lower layer); the lower layer may include a lowervoxel size than the higher layer.

In 606, the filtered reconstructed image matrixes in different layersmay be merged. The classification information of the image matrixes maybe recorded in the DICOM file. The image matrixes in different layersmay be stored in different matrixes. The image matrixes may be merged toform a merged image matrix based on its classification information. Insome embodiments, the merging of image matrixes may be performedaccording to process 600 b as illustrated in FIG. 6B.

In 608, post-processing module 230 may store image matrixes withdifferent voxel sizes into different matrixes to be merged according toclassification information. In some embodiments, a physical region maycorrespond to a plurality of matrixes to be merged. The plurality ofmatrixes to be merged may have different voxel sizes. The images thatcorrespond to two or more image matrixes to be merged may at leastpartially overlap or do not overlap at all.

In 610, post-processing module 230 may create a merged matrix M. Thephysical region corresponding to the merged matrix M may be the largestregion of the physical regions corresponding to the image matrixes to bemerged. In some embodiments, the voxel size of the merged matrix M maybe the smallest voxel of the voxel sizes of the image matrixes to bemerged. In some embodiments, the smaller the voxel size, the higher theresolution is.

In 612, post-processing module 230 may generate a final image matrixafter the determination of the merged matrix M and its correspondingphysical region and the voxel size. All or parts of the matrix whosecorresponding physical region is smaller than the largest physicalregion of the image matrixes to be merged may be filled by zeroes. Forinstance, the matrix whose corresponding physical region is smaller thanthe largest physical region of the image matrixes to be merged may beextended from its original form to match the largest physical region. Atleast a portion of the image matrix that is extended may be filled byzeroes. In some embodiments, matrixes whose voxel sizes are greater thanthe smallest voxel size of matrixes to be merged may be interpolated bypost-processing module 230. The interpolation of the matrixes mayinclude estimating a size of the voxel that is null in thehigh-resolution image when a low-resolution image is converted to ahigh-resolution image based on an interpolation algorithm. Theinterpolation algorithm may include a bilinear interpolation algorithm,a bi-cubic interpolation algorithm, a fractal interpolation algorithm, anatural neighbor interpolation algorithm, a nearest neighborinterpolation algorithm, a minimum curvature algorithm, a localpolynomial regression algorithm, or the like, or a combination thereof.

Post-processing module 230 may merge the filled and/or interpolatedimage matrixes to form a final matrix M. In some embodiments, the imagematrixes to be merged may be classified into one or more layers. Forexample, the image matrixes to be merged may be classified into Llayers. Image matrixes in the first layer, image matrixes in the secondlayer, . . . image matrixes in the L^(th) layer may be filled into thematrix M successively.

Merely by way of example, image matrix A may be classified into layer Xand image matrix B may be classified into layer Y. Y may be larger thanX. In some embodiments, image matrixes may be classified according toits voxel sizes. The voxel size of image matrix B in layer Y is smallerthan the voxel size of image matrix A in layer X. If the physical regioncorresponding to image matrix A and the physical region corresponding toimage matrix B do not overlap, the element values of the two imagematrixes may be respectively filled into the matrix M. If the physicalregion corresponding to image matrix A and the physical regioncorresponding to image matrix B at least partially overlap with eachother, the element values of the matrix B may be filled into the matrixM.

FIG. 7A and FIG. 7B are schematic diagrams illustrating a correlationbetween voxels and matrixes according to some embodiments of the presentdisclosure. In some embodiments, the correlation between image matrixesand voxels may be recorded in a lookup table. As shown in FIG. 7A andFIG. 7B, M₀ and M₁ are two image matrixes. Voxel 740 in M₀ maycorrespond to eight voxels 720 in M₁. The voxel size of image matrix M₁is smaller than the voxel size of image matrix M₀. In some embodiments,region 730 may be covered by the region corresponding to the imagematrix M₀ and the region corresponding to the image matrix M₁.

The contribution of the voxel 740 in M₀ to the number of lines ofresponse i may be calculated based on the contribution of the eightvoxels 720 in M₁ to the number of lines of response i. The eight voxels720 in M₁ may correspond to voxel 740 in M₀. The eight voxels 720 may bedetermined based on a lookup table (LUT). The lookup table may recordthe correlation between the voxels in one or more image matrixes.

For example, the lookup table may record a correlation between voxel 740(i.e., M₀(X,Y,Z)) and eight voxels 720 (i.e., M₁(X₁,Y₁,Z₁),M₁(X₁,Y₂,Z₁), M₁(X₂,Y₁,Z₁), M₁(X₂,Y₂,Z₁), M₁(X₁,Y₁,Z₂) M₁(X₁,Y₂,Z₂),M₁(X₂,Y₁,Z₂), M₁(X₂,Y₂,Z₂)).

In some embodiments, image matrixes M₀ and M₁ may be classified intodifferent layers based on their voxel values. The correlation betweenthe image matrixes in different layers recorded in the lookup table maybe determined according to the position relationship between the imageregions of the image matrixes.

In some embodiments, the lookup table may record rearrangementinformation of an image matrix. For example, the lookup table may recordthe correlation between the compressed and/or rearranged voxels and theelements in the image matrix M₀.

FIG. 8 is a schematic diagram illustrating a matching of imaging modulesaccording to some embodiments of the present disclosure. In someembodiments, imaging device 110 may include one or more imaging modules.One or more detectors of the one or more imaging modules may be placedcontinuously around the imaged object. Merely by way of examples, theimaging module may correspond to a PET detector. More descriptionsregarding the position arrangement of detectors may be found elsewherein the present disclosure. See, e.g., FIG. 10A to FIG. 10C and thedescription thereof.

As shown in FIG. 8 , the imaging device 110 may include six imagingmodules. The six imaging modules may be paired to form twenty oneimaging module pairs including imaging module pair 810, imaging modulepair 820, and imaging module pair 830. Imaging module pair 810 may be apair including a sixth imaging module and a sixth imaging module. A lineof response may be received by the detectors of the sixth imagingmodule. Imaging module pair 820 may be a pair including a first imagingmodule with a sixth imaging module. A line of response may be receivedby the detectors corresponding to the first imaging module and the sixthimaging module. Imaging module pair 830 may be a pair including a firstimaging module with a fourth imaging module. A line of response may bereceived by the detectors corresponding to the first imaging module andthe fourth imaging module. In some embodiments, the computational tasksof each imaging module pair may be processed by the subordinatecomputing node(s) described in elsewhere in the present disclosure. Insome embodiments, the computational tasks of each module pair may beperformed by a subordinate compute node. The computation results of oneor more subordinate computing nodes may be summarized by one or moremain computing node.

As shown in FIG. 8 , a black portion, such as the “x” shape or “-” shapein a rectangular box, may correspond to an element in an image matrix tobe modified in the matching of the imaging modules. More descriptionsregarding the element modification may be found elsewhere in the presentdisclosure. See, e.g., FIG. 10A to FIG. 10C and the description thereof.In some embodiments, an image matrix may be compressed and/or rearrangedbased on the element that needs to be modified to reduce the storage andamount of calculation of a pair of imaging modules. For example, theelements below the black line 840 in imaging module pair 810 may beremoved. As another example, the elements in the black line 850 andblack line 860 in imaging module pair 820 may be moved together, andthen the rest elements except black line 850 and black line 860 inimaging module pair 820 may be removed.

FIG. 9 is a block diagram of an exemplary image matrix processing unitaccording to some embodiments of the present disclosure. Image matrixprocessing unit 350 may include an image matrix compression sub-unit910, an image matrix rearrangement sub-unit 920, an image matrixinversely rearrangement sub-unit 930, an image matrix decompressionsub-unit 940, and a lookup table generation sub-unit 950.

Image matrix compression sub-unit 910 may compress an image matrix. Forexample, one or more elements may be removed in image matrixcompression. In some embodiments, the removed element may be null. In aPET system, a null element may correspond to a voxel that is notpenetrated by a line of response, or a voxel that does not contribute toa count of a detector in the process of image reconstruction (e.g.,forward projection, back projection, etc.). In some embodiments, theremoved element may satisfy one or more conditions. For example, theremoved element may be an element whose value is below a threshold. Asanother example, the removed element may be an element that is locatedin a certain position in a matrix (e.g., a position that may not affectimage reconstruction or other operations, etc.). Examples of suchpositions include a table that supports a patient in a scan.

Image matrix rearrangement sub-unit 920 may move an element in an imagematrix from a first position to a second position. An element with acertain characteristic may be rearranged. For example, non-zero elementsin an image matrix may be moved and gathered. In some embodiments, theelement originally at the second position may be removed before theimage matrix rearrangement. In some embodiments, the element originallyat the second position may be moved to the first position.

Image matrix inversely rearrangement sub-unit 930 may move all or aportion of the moved elements to their original positions before therearrangement. In some embodiments, the value of an element in an imagematrix may remain the same when it is rearranged or inverselyrearranged.

Image matrix decompression sub-unit 940 may add one or more elementsinto an image matrix. In some embodiments, one or more elements that areremoved from an image matrix compression may be added back to theiroriginal positions. In some embodiments, the value of an element mayremain the same in image matrix compression and/or image matrixdecompression.

Lookup table generating sub-unit 950 may create a lookup table. Thelookup table may record position information about image matrixrearrangement, and/or correlation between the elements in one or moreimage matrixes. For example, the lookup table may record image matrixesin different layers as shown in FIG. 7A and FIG. 7B, and the correlationbetween elements in image matrixes in different layers and imageregions.

For persons having ordinary skills in the art, multiple variations andmodifications in form and detail may be made without departing from theprinciples in the present disclosure. However, those variations andmodifications also fall within the scope of the present disclosure. Forexample, lookup table generating sub-unit 950 and image matrixrearrangement sub-unit 920 may form a sub-unit that may perform thefunction of the two sub-units.

FIG. 10A, FIG. 10B, and FIG. 10C are schematic diagrams illustrating anexemplary process for processing an image matrix according to someembodiments of the present disclosure. Image matrix 1010 may correspondto a scanned region. The scanned region may be determined by a firstimaging module 1011, a second imaging module 1012, a third imagingmodule 1013, and a fourth imaging module 1014. First imaging module 1011and fourth imaging module 1014 may be paired. First imaging module 1011may correspond to a first detector. Fourth imaging module 1014 maycorrespond to a fourth detector.

In some embodiments, a subordinate computing node may be used to processthe computational tasks of the first imaging module and the fourthimaging module. The subordinate computing node may determine the linesof response which can be received by the detectors of the first imagingmodule and the fourth imaging module. As shown in FIG. 10A, the shadedportion 1015 in the image matrix 1010 may correspond to an element thatneeds to be updated and calculated in reconstruction after the matchingof first imaging module 1011 and fourth imaging module 1014. The valueof the rest elements in image matrix 1010 may remain the same in matrixreconstruction.

In some embodiments, the image matrix 1010 may be compressed into animage matrix 1020 as illustrated in FIG. 10B. For example, the elementsin the upper and lower portions of image matrix 1010 whose values remainthe same in the image matrix reconstruction may be removed. Merely byway of example, elements Z₁, Z₂, Z₃, Z₁₈, Z₁₉, and Z₂₀ may be removed.

In some embodiments, image matrix 1020 may be rearranged and compressedinto image matrix 1030 as illustrated in FIG. 10C. For example, elementsin the image matrix 1020 whose values have been changed in the imagematrix reconstruction may be moved and gathered. For example, elementsin every T-dimension in the image matrix 1020 may be processed. Forexample, elements (T₁, Z₉), (T₁, Z₁₀), (T₁, Z₁₁), (T₁, Z₁₂) in the imagematrix 1020 may be removed, and the rest elements in T₁ may be moved. Insome embodiments, the element to be removed and its moving direction andposition may be determined according to a lookup table.

As shown in FIG. 10A through FIG. 10C, image matrix 1010 (10×20) iscompressed and rearranged into the image matrix 1030 (10×10) to reducethe storage and amount of calculation. The compression and rearrangementof the image matrix may not influence the reconstruction of the imagematrix. In some embodiments, the compressed and rearranged image matrixmay be stored in storage module 250. The information related to imagematrix compression and rearrangement may be recorded in a lookup table,or stored in storage module 250.

FIG. 11 is a flowchart illustrating an exemplary process forreconstructing an image matrix according to some embodiments of thepresent disclosure. Process 1100 may be performed by reconstructionmodule 220. In 1102, a main computing node and a subordinate computingnode may be determined. The subordinate computing node may determine asub-image matrix. The sub-image matrix may correspond to a sub-scannedregion. For example, the sub-scanned region may be scanned by one ormore detectors.

In some embodiments, the subordinate computing node may determine thevalues of one or more elements in a sub-image matrix corresponding to asub-scanned region based on the count of detector corresponding to thesub-scanned region. In some embodiments, the subordinate computing nodemay perform computational task of an image matrix corresponding to animaging module pair. In some embodiments, the main computing node maymerge and gather the computing results of one or more subordinatecomputing nodes.

In 1104, the computational tasks of image matrixes may be distributed todifferent subordinate compute nodes. In some embodiments, eachsubordinate compute node may determine an image matrix corresponding toa module pair.

In 1106 to 1108, an image matrix corresponding to an imaging module pairmay be compressed and rearranged. More descriptions regarding therearrangement and compression of an image matrix may be found elsewherein the present disclosure. See, e.g., FIG. 9 through FIG. 10C, and thedescription thereof. Different image matrixes may be compressed andrearranged in different manners because different image matrixes maycorrespond to different imaging module pairs. As shown in FIG. 10A, asubordinate computing node may determine an image matrix correspondingto first imaging module 1011 and fourth imaging module 1014. The imagematrix may be compressed and rearranged. The manner of image matrixcompression and rearrangement may be determined based on the shadedportion between first imaging module 1011 and fourth imaging module1014.

In some embodiments, a subordinate computing node may determine an imagematrix corresponding to first imaging module 1011 (i.e. a rectangularregion defined by the first detector). The subordinate computing nodemay compress the corresponding image matrix and determine the voxelsizes in the region corresponding to first imaging module 1011.

In 1110, a forward projection and/or a back projection may be performedbased on a subset of a subordinate computing node. The forwardprojection may be performed to determine the counts of detectors along aline of response corresponding to a pair of imaging module based on theimage matrix to be reconstructed. The back projection may be performedto determine the element value in the image matrix to be reconstructedbased on the counts of detectors along the line of responsecorresponding to a pair of imaging module. Information related tocoordinate transformation of the rearranged image matrix may be obtainedfrom a lookup table.

In some embodiments, projection data may be divided into a plurality ofgroups. One or more groups of the plurality of groups may be a subset.For example, the projection data may be grouped according to theprojection direction. In some embodiments, the image to be reconstructedmay include a plurality of image matrixes corresponding to differentlayers. As shown in FIG. 4 , image matrixes corresponding to differentlayers may have different voxel sizes. A line of response may penetratea region that corresponds to the image matrixes of different layers. Thecontribution of one or more elements to the line of response may becalculated based on information related to the image matrixes ofdifferent layers which may be recorded in a lookup table. In someembodiments, the lookup table may record the relationships between theelements in one or more image matrixes.

In 1112, the rearranged image matrix may be rearranged inversely afterthe determination of element values. Operation 1112 may be performed byreconstruction module 220. The rearranged image matrix may betransformed back to the image matrix corresponding to the physicalregion.

In 1114, a judgment may be made as to whether back projections in allangles of the subset have been performed and merged. The backprojections in all angles may refer to the back projections in allangles of the matched pair of imaging module. If not, 1106 to 1112 maybe repeated, and the image matrix may be processed in a different angle.Otherwise, 1116 may be performed. In 1116, the image matrix may bedecompressed by reconstruction module 220. The size of the image matrixafter decompression may be the same as the size of the image matrixbefore compression.

In 1118, the main computing node may merge the back projection resultsof the subordinate computing nodes. In some embodiments, thedecompressed image matrixes at different angels may have the same size.The main computing node may merge the decompressed image matrixes atdifferent angles to generate a merged image matrix. Merely by way ofexample, the values of elements at the same positions in decompressedimage matrixes at different angles may be added together to generate themerged image matrix.

In 1120, the main computing node may update the image matrix based onthe merged matrix and start to process a next subset. The constructionof the subset may be finished after the image matrix is updated. In someembodiments, reconstruction module 220 may reconstruct the image matrixbased on next subset and update the image matrix based on thereconstruction result until all the subsets are processed. If all thesubsets have been processed, 1124 and other subsequent steps (if any)may be performed. Otherwise, 1106 to 1120 may be performed again untilall the subsets are processed. In some embodiments, an Ordered SubsetExpectation Maximization (OSEM) algorithm may be applied to reconstructthe image matrix. When all the subsets are processed, the reconstructedimage matrix may be output, and the iteration may be finished.

In 1124, a judgment may be made as to whether the termination criterionfor the iteration has been met. If the termination criterion has beenmet, the iteration may be stopped. Otherwise, 1104 to 1124 may continueto be performed for next iteration. The termination criterion ofiteration may be determined based on the image matrix to bereconstructed, or determined by a user. For example, the iteration maybe ended when the difference between the reconstructed image matrix inthe current iteration and that in the last iteration is less than apre-set threshold. As another example, the iteration may be ended whenthe reconstructed image matrix in the current iteration meets a certaincondition. As another example, the iteration may be ended when thenumber of iterations is larger than the pre-set number of iterations.

For persons having ordinary skills in the art, various modifications andchanges in the forms and details of the application of the above methodand system may occur without departing from the principles in thepresent disclosure. However, those variations and modifications alsofall within the scope of the present disclosure. For example, a model offorward point spread function may be used before rearranging the imagematrix and a backward point diffusion model may be used beforerearranging the matrix inversely to amend the process of imagereconstruction.

FIG. 12 is a flowchart illustrating an exemplary process for processingan image matrix according to some embodiments of the present disclosure.Process 1200 may be performed by image matrix processing unit 350. Insome embodiments, process 1200 described with reference to FIG. 12 maybe an exemplary process for achieving 1106 to 1114 as shown in FIG. 11 .In some embodiments, an imaging device may include one or more imagingmodules. For example, one or more detectors of the one or more imagingmodules may be continuously placed around the object. In someembodiments, the computational tasks of a pair of imaging modules may beperformed by a subordinate computing nodes. The computation results ofsubordinate computing nodes may be collected and merged by a maincomputing node. In some embodiments, a pair of imaging modules maycorrespond to an image matrix. In 1202, image matrix processing unit 350may compress the image matrix corresponding to a pair of imaging modulesto generate a third image matrix.

In 1204, the third image matrix may be rotated clockwise. In 1206, imagematrix processing unit 350 may determine the position of a basic layerand an effective matrix scope based on the pair of imaging modulescorresponding to the third image matrix. The position of a basic layerand the effective matrix scope may refer to the moving position anddirection of the element in the third image matrix in subsequentrearrangement steps.

In 1208, a lookup table may be created. The moving position anddirection of the element in the third image matrix may be stored in thelookup table. Operation 1208 may be performed by matrix processing unit350.

In 1210, image matrix processing unit 350 may generate a fourth imagematrix based on the lookup table and the third image matrix. In someembodiments, the third image matrix may be rearranged to generate thefourth image matrix.

In 1212, a forward projection may be performed on the fourth imagematrix to generate a projection matrix. In 1214, image matrix processingunit 350 may determine a correction coefficient based on the result ofthe forward projection. The correction coefficient may include a ratioof the counts of detectors along a line of response to the forwardprojection of the fourth image matrix along the line of response.

In 1216, the back projection may be performed on the projection matrixto generate a fifth image matrix. In some embodiments, the fifth imagematrix may be generated based on the correction coefficient.

In 1218, image matrix processing unit 350 may rearrange the fifth imagematrix inversely to generate a sixth image matrix. In 1220, the sixthimage matrix may be rotated anticlockwise. In some embodiments, thedirection and size of the third image matrix may be the same as thesixth image matrix.

Having described the basic concepts, it may be rather apparent to thoseskilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context comprising any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(comprising firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “block,” “module,” “unit,” “component,” “device” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or more computerreadable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, comprising electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, comprising wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, comprising an object oriented programminglanguage such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#,VB. NET, Python or the like, conventional procedural programminglanguages, such as the “C” programming language, Visual Basic, Fortran2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages such asPython, Ruby and Groovy, or other programming languages. The programcode may execute entirely on the user's computer, partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, comprisinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider) or in a cloud computingenvironment or offered as a service such as a Software as a Service(SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities of ingredients,properties, and so forth, used to describe and claim certain embodimentsof the application are to be understood as being modified in someinstances by the term “about,” “approximate,” or “substantially.” Forexample, “about,” “approximate,” or “substantially” may indicate ±20%variation of the value it describes, unless otherwise stated.Accordingly, in some embodiments, the numerical parameters set forth inthe written description and attached claims are approximations that mayvary depending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the descriptions, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

We claim:
 1. An image reconstruction method for an imaging system, themethod being implemented on a computing device having one or moreprocessors and one or more storage media, the method comprising:determining a first region and a second region of an object; acquiring,using the imaging system, scan data of the object including the firstregion and the second region; simultaneously reconstructing a firstregional image of the first region and a second regional image of thesecond region based on the scan data, wherein a resolution of the firstregional image and a resolution of the second regional image aredifferent, and wherein the simultaneously reconstructing a firstregional image of the first region and a second regional image of thesecond region based on the scan data comprises: determining a firstimage matrix corresponding to the first region and a second image matrixcorresponding to the second region; and generating the first regionalimage and the second regional image by reconstructing the first imagematrix and the second image matrix based on the scan data.
 2. The methodof claim 1, wherein the first region corresponds to first voxels of afirst voxel size, the second region corresponds to second voxels of asecond voxel size different from the first voxel size, the first imagematrix including first elements corresponding to the first voxels, thesecond image matrix including second elements corresponding to thesecond voxels.
 3. The method of claim 2, wherein the generating thefirst regional image and the second regional image by reconstructing thefirst image matrix and the second image matrix based on the scan datafurther comprises: performing a forward projection on the first elementscorresponding to the first voxels and the second elements correspondingto the second voxels to generate projection data; and generating thefirst regional image by performing a back projection on the projectiondata corresponding to the first voxels.
 4. The method of claim 3,wherein the generating the first regional image and the second regionalimage by reconstructing the first image matrix and the second imagematrix based on the scan data further comprises: generating the secondregional image by performing a back projection on the projection datacorresponding to the second voxels.
 5. The method of claim 2, furthercomprising: performing post-processing operation on the reconstructedfirst image matrix and the reconstructed second image matrix.
 6. Themethod of claim 5, wherein the post-processing operation comprises:performing a first filtering on the reconstructed first image matrix togenerate a first filtered reconstructed image matrix; performing asecond filtering on the reconstructed second image matrix to generate asecond filtered reconstructed image matrix; classifying the firstfiltered reconstructed image matrix and the second filteredreconstructed image matrix into one or more layers; and generating amerged matrix by merging the first filtered reconstructed image matrixand the second filtered reconstructed image matrix in the one or morelayers.
 7. The method of claim 2, wherein the generating the firstregional image and the second regional image by reconstructing the firstimage matrix and the second image matrix based on the scan datacomprises: iteratively reconstructing the first image matrix based onthe scan data for a first number of iterations, and iterativelyreconstructing the second image matrix based on the scan data for asecond number of iterations.
 8. The method of claim 7, wherein the firstnumber of iterations is different from the second number of iterations.9. The method of claim 2, wherein the reconstructing the first imagematrix and the second image matrix is performed based on an OrderedSubset Expectation Maximization algorithm.
 10. The method of claim 2,further comprising: generating a lookup table, wherein the lookup tablerecords at least one of a correlation between the first image matrix andthe first voxels, or a correlation between the second image matrix andthe second voxels.
 11. The method of claim 1, further comprising:acquiring structure information of the object; and determining the firstregion and the second region based on the structure information.
 12. Asystem for image reconstruction comprising: at least one storage deviceincluding a set of instructions for image reconstruction; and at leastone processor configured to communicate with the at least one storagemedium, wherein when executing the set of instructions, the at least oneprocessor is configured to direct the system to perform operationsincluding: determining a first region and a second region of an object,acquiring scan data of the object including the first region and thesecond region; simultaneously reconstructing a first regional image ofthe first region and a second regional image of the second region basedon the scan data, wherein a resolution of the first regional image and aresolution of the second regional image are different, and wherein thesimultaneously reconstructing a first regional image of the first regionand a second regional image of the second region based on the scan datacomprises: determining a first image matrix corresponding to the firstregion and a second image matrix corresponding to the second region; andgenerating the first regional image and the second regional image byreconstructing the first image matrix and the second image matrix basedon the scan data.
 13. The system of claim 12, wherein the first regioncorresponds to first voxels of a first voxel size, the second regioncorresponds to second voxels of a second voxel size different from thefirst voxel size, the first image matrix including first elementscorresponding to the first voxels, the second image matrix includingsecond elements corresponding to the second voxels.
 14. The system ofclaim 13, wherein the generating the first regional image and the secondregional image by reconstructing the first image matrix and the secondimage matrix based on the scan data comprises: performing a forwardprojection on the first elements corresponding to the first voxels andthe second elements corresponding to the second voxels to generateprojection data; and generating the first regional image by performing aback projection on the projection data corresponding to the firstvoxels.
 15. The system of claim 14, wherein the generating the firstregional image and the second regional image by reconstructing the firstimage matrix and the second image matrix based on the scan datacomprises: generating the second regional image by performing a backprojection on the projection data corresponding to the second voxels.16. The system of claim 13, wherein the at least one processor isfurther configured to direct the system to perform additional operationsincluding: performing post-processing operation on the reconstructedfirst image matrix and the reconstructed second image matrix.
 17. Thesystem of claim 16, wherein the post-processing operation comprises:performing a first filtering on the reconstructed first image matrix togenerate a first filtered reconstructed image matrix; performing asecond filtering on the reconstructed second image matrix to generate asecond filtered reconstructed image matrix; classifying the firstfiltered reconstructed image matrix and the second filteredreconstructed image matrix into one or more layers; and generating amerged matrix by merging the first filtered reconstructed image matrixand the second filtered reconstructed image matrix in the one or morelayers.
 18. The system of claim 13, wherein the generating the firstregional image and the second regional image by reconstructing the firstimage matrix and the second image matrix based on the scan datacomprises: iteratively reconstructing the first image matrix based onthe scan data for a first number of iterations, and iterativelyreconstructing the second image matrix based on the scan data for asecond number of iterations.
 19. The system of claim 18, wherein thefirst number of iterations is different from the second number ofiterations.
 20. A non-transitory computer readable medium, comprising aset of instructions for image stitching, wherein when executed by atleast one processor, the set of instructions direct the at least oneprocessor to effectuate a method, the method comprising: determining afirst region and a second region of an object; acquiring, using theimaging system, scan data of the object including the first region andthe second region; simultaneously reconstructing a first regional imageof the first region and a second regional image of the second regionbased on the scan data, wherein a resolution of the first regional imageand a resolution of the second regional image are different, and whereinthe simultaneously reconstructing a first regional image of the firstregion and a second regional image of the second region based on thescan data comprises: determining a first image matrix corresponding tothe first region and a second image matrix corresponding to the secondregion; and generating the first regional image and the second regionalimage by reconstructing the first image matrix and the second imagematrix based on the scan data.